Data sensor validation system and method

ABSTRACT

A method of data sensor validation is disclosed. The method comprises the steps of pre-processing data sensor from each sensor from a plurality of sensors for at least segmenting the data sensors into a plurality of groups, each group for grouping sensors for sensing highly relevant data one to another; providing the pre-processed data sensor to a correlation processor, the correlation processor for determining from pre-processed data sensor, pre-processed data that is other than correlated, the determination made in dependence upon redundant pre-processed data other than pre-processed data from two sensors for sensing an identical parameter; and, when pre-processed data that is other than correlated is detected, providing an indication to an operator that the sensor data is other than correlated. Advantageously the method is applicable for use in geographically remote sensor applications wherein the sensors are adapted to provide geographic location information using a global positioning system to the correlation processor through wireless communication with satellites.

This application claims priority from the U.S. Provisional ApplicationSer. No.: 60/298,406 filed Jun. 18, 2001.

FIELD OF THE INVENTION

The invention relates generally to data sensor validation and moreparticularly to a method of data sensor validation for use inenvironmental applications.

BACKGROUND OF THE INVENTION

A neural network is an artificial neural circuit network that, either incircuitry or in software, performs correlation processing. In a typicalneural network, there is one or more intermediate layers between a datainput layer and a data output layer, each of these layers being made upof a plurality of units, network-like connections being made between theinput/output sections and the intermediate layers by means of theinput/output systems. Because this neural network has non-linearcomponents, it is capable of performing extremely complex correlationswith respect to a variety of data types. These correlations are thenuseful in determining approximations, projections, and so forth. Becauseof this, neural networks are currently used in many industries,including manufacturing and service industries.

In these industries, a neural network is selected for a particularprocess and is then trained using known input data and known outputresponses. For example, in a process control circuit, a neural networkis trained to provide a desired process control signal in response to aplurality of sensor data received at an input port thereof. Throughtraining, weights within the neural network are modified to ensure thateach sensor input value is appropriately accounted for in the controlsignal provided at the output of the neural network. Of course, someneural networks are manufactured with their weights integrated thereinwhen their use is known and fixed.

Conventional neural network training and testing methods requirecomplete patterns such that they are required to discard patterns withmissing or bad data. Experimental results have shown that neural networktesting performance generally increases with more training data whentrained.

Most methods of training and using neural networks do not account forthe relationship between measurements by one sensor relative to anothersensor measurement in unrelated systems. Often, in conjunction withincreasing the reliability of measurement data, fault detectiontechniques such as sensor redundancy are used to increase a controlsystem's ability to recognize that measurement data is unreliable. Ifmeasurement data from a sensor in a group of redundant sensors isinconsistent with measurement data from other sensors in the group, theinconsistent data is considered as unreliable and are ignore.

Data sensor validation is an important part of feedback based controlsystems and of large scale monitoring systems. In data sensorvalidation, data from each of a plurality of sensors is validated toavoid decisions or monitoring being dependent upon erroneous sensordata. Effective detection of erroneous measurements and recovery ofmissing data are important as erroneous or missing data may disruptoperations and may cause severely abnormal operating conditions andresult in incorrect safety, control, and economic decisions.

When a neural network is trained, weighting coefficients and biases arerandomly applied with respect to the input data for each of units thataccepts data. As data is input under these conditions, judgments aremade with regard to the correctness of the output resulting fromcalculation according to these weighting coefficients. Whether or notthe output results are correct is fed back using a learning method suchas back-propagation, the originally set weighting coefficients andbiases being corrected, and data being re-input. By repeating thisprocess of input and correction of weighting coefficients and biases alarge number of times, the weighting coefficients and biases that willobtain an appropriate output for a prescribed data input areestablished.

By installing a trained neural network into character recognition, imageprocessing or other system that is implemented by a computer, the neuralnetwork can be put into practical use in many industries, includingmanufacturing and service industries. These neural networks are used inclosed environments wherein the sensors sense known parameters as forexample the amount of carbon monoxide or other gases along amanufacturing process. Such a restricted environment facilitates theidentification and the replacement or repair/adjustment/calibration of afaulty sensor when erroneous data are sensed.

Conversely, when considering large-scale neural network, i.e. open fieldcontrol system, it is important to precisely point out which sensor isdeficient when erroneous data are received at a control operatingsystem. Since in an open field neural network the sensors are remotelylocated, sending a technician to an isolated remote location forreplacing a faulty sensor is an expensive process that mostorganizations tend to avoid if the faulty sensor is not preciselyidentified.

Furthermore, a major problem with existing validation system usingneural network is when a sensor data is close to an extreme value—loweror upper—within or outside a pre-determined range of values, the sensordata is attributed a value corresponding to an extreme value, withoutconsideration of the real value of the sensor data validation of thesensor data. Therefore, the attributed value is not representative of anevent occurring at the sensor, there is no indication to which extremethe sensed value is close to.

It would be advantageous to provide a method of validating data that isimproved over the data limit approach but not as costly to implement asthe duplicate sensor approach.

Furthermore, it would be advantageous to provide a method for suggestinga value for replacing a sensed data, which shows a shift from apredictable sensed data.

OBJECT OF THE INVENTION

It is another object of the present invention to provide a method forverifying the validity of sensor data for use in environmental typeapplications.

SUMMARY OF THE INVENTION

In accordance with the invention there is provided a method of datasensor validation comprising the steps of: pre-processing data sensorfrom each sensor from a plurality of sensors for at least segmenting thedata sensors into a plurality of groups, each group for grouping sensorsfor sensing highly relevant data one to another; providing thepre-processed data sensor to a correlation processor, the correlationprocessor for determining from pre-processed data sensor, pre-processeddata that is other than correlated, the determination made in dependenceupon redundant pre-processed data other than pre-processed data from twosensors for sensing an identical parameter; and, when pre-processed datathat is other than correlated is detected, providing an indication to anoperator that the sensor data is other than correlated.

In accordance with the invention there is provided a method of datasensor validation comprising the steps of: pre-processing data sensorfrom each sensor from a plurality of sensors; providing thepre-processed data sensor to a correlation processor, the correlationprocessor for determining from pre-processed data sensor, pre-processeddata that is other than correlated, the determination made in dependenceupon redundant pre-processed data other than pre-processed data from twosensors for sensing an identical parameter; and, when pre-processed datathat is other than correlated is detected, providing an indication to anoperator that the sensor data is other than correlated.

In accordance with the invention there is provided a sensor for use ingeographically remote sensor applications comprising a sensing circuitryfor sensing data; a transmitter for transmitting sensed data to acorrelation processor, the correlation processor for determining frompre-processed sensed data, pre-processed data that is other thancorrelated, the determination made in dependence upon redundantpre-processed data other than pre-processed data from two sensors forsensing an identical parameter at an approximately same geographiclocation; a wireless transceiver circuit for wirelessly determining ageographic location of the sensor, for transmitting the determinedgeographic location of the sensor to the correlation processor, and fortransmitting the sensed data to the correlation processor for allowingthe correlation processor to associate the received sensed data with thedetermined geographic location.

DESCRIPTION OF THE DRAWINGS

Exemplary embodiments of the invention will now be described inconjunction with the following drawings, in which similar referencenumbers designate similar items:

FIG. 1 is an example of an open field sensors organization according toan embodiment of the present invention;

FIG. 2 is an example of an event, which modifies sensor readings incascade;

FIG. 3 is a flow chart diagram of a method of grouping sensors;

FIGS. 4a and 4 b illustrate a flow chart diagram of a method ofpre-processing sensor data according to the present invention; and

FIG. 5 is a flow chart diagram of a testing the pre-processing modelaccording to the present invention.

DESCRIPTION OF THE INVENTION

The method is described below with reference to water flow sensorsthough it to applicable to any environmental or distributed system withcorrelation between different sensed values.

Referring to FIG. 1, a hydraulic network is illustrated showing fourrivers R₁-R₄, wherein three rivers R₂, R₃ and R₄ are confluent to riverR₁, and a dam D₁ regulates the water flow from R₂, R₃ and R₄ toward R₁.Similarly, each of the rivers R₂, R₃ and R₄ has a water flow regulatorin a form of a dam D₂, D₃ and D₄, respectively, upstream the dam D₁.Each dam is equipped with generator sensors for sensing for example theamount of electricity generated according to an opening of the gates ofeach gate of the dam. Each river is equipped with sensors S₁-S₄,respectively, such as upstream Su₁-Su₄, downstream Sd₁-Sd₄ water levelsensors, and flow rate sensors, sensor for sensing chemical compositionof water, power output sensors and other sensors. Such a system forregulating flow rate or water distribution is known and conventionallyused. However, based upon data sensed at a dam, a decision is made andan action is taken accordingly by an operator. Therefore, when theoperator does not react properly in an objective sense to the actualevents, the consequences are potentially catastrophic. For example, whensensors sense a dam overflow of a dam located a few kilometers from acity, it is too late to counteract to prevent inundations in the city.

Conventionally, the sensors are sensors available on the market and aremanually installed at the various location from which data arepotentially interesting to sensed, for example at the top of the dam forsensing an over flowing or downstream the dam for sensing a water levelfor example at this specific location. Of course, because of the use ofsensors for building neural networks of such a large scale, the numberof sensors used is also extremely large. Advantageously, the use ofenough sensors provides a lot of information for training the neuralnetwork and eventually allows for compensating for failure of somesensors. Advantageously, when enough sensors are in use, the trainedcorrelation system operates with a substantial amount of redundantinformation, though perhaps not evidently so, and each sensor isrequired in order to avoid “guessing” that normal operation isoccurring. When a sensor fails, the ability to correlate the redundantinformation received from other sensors that are not same sensors as thefailed sensor allows for accurate estimation of the failed sensor'sproper sensed data.

Data sensed from the sensors located on each rivers of the hydraulicnetwork are gathered, computerized, organized, cross-referenced during aperiod of time long enough to ascertain the validity of the data sensedand therefore the validity of the sensors. Correlation between datasensed from each of the sensors Su₁-Su₄ and Sd₁-Sd₄ are established totrain the neural network to make short-term predictions for missingdata, to evaluate events and to identify invalid data. The trainedneural network is also useful for studying complex relationships betweensensors that are pseudo-redundant.

If different water level sensors Sd₃′, Sd₃″ and Sd₃′″ measure waterlevels within river R₃ as shown in FIG. 1 for example, the rising of thewater level at first sensor Sd₃′ will result in a rising of the waterlevel at the second sensor Sd₃″ after a known period of time haselapsed, which will result in a rising of the water level at the thirdsensor Sd₃′″ after another known period of time has elapsed.

In an event of sensor failure, the knowledge of regularities in mutualsensor dependencies is used to predict correct data for the failedsensor without relying on sensor duplication—two sensors for sensing anexact same parameter at an approximately same location and time.Referring to FIG. 2, an example of an event that modifies the regularflow of river R₃ is shown. The second sensor Sd₃,″ does not indicate arisen water level whereas the third downstream sensor Sd₃′″ does, thenit is evident that either the second sensor was bypassed—another branchBP of the river exists between the first sensor Sd₃′ and the thirdsensor Sd₃′″—or that the second sensor Sd₃″ is failed. Though thisexample is very simple, a correlation processor in the form of a neuralnetwork could process much more complicated relationships. In the aboveexample, rain sensors, temperature sensors, ground water level sensors,water flow speed sensors; chemical sensors for water chemical contentand so forth can be incorporated in order to enhance the correlationbetween sensor output values.

Optionally, the sensors are equipped with wireless communicationtransmitter for communicating with satellite for example. Such acommunication system allows the sensors to be precisely localized usinga Global Positioning System (GPS) transmitting the information to theneural network. Therefore, as soon as a sensor is installed, it beginsto sense and transmit data to a control system of the neural network,which knows exactly where the received data come from.

To prevent potential disasters and to prevent actions based on erroneousdata due to a faulty sensor, the sensor system is organized to form acorrelation processor in the form for example of a neural network.Advantageously, data from each sensor are extracted and pre-processedduring a data mining process, which is defined as a nontrivialextraction of implicit, previously unknown, and potentially usefulinformation from data. The data mining process consists of two majorparts namely a training process and a testing process.

During the training process two models, i.e. a prediction model and aregression models are created. The purpose of the prediction model is tofill in missing data both during the training phase and during thetesting phase prior to using the data in the regression model. Theregression model restores the missing data and validates existing data.Both the regression model and prediction models are prepared iterativelyin the training process. Once they are created for the training processthey are used for the training process.

Optionally, the prediction model is not performed during the trainingprocess.

Referring to FIG. 3, a flow chart diagram of a method of groupingsensors is shown. A neural network is composed of a number ofinterconnected units each comprising an input and an output. The outputof each unit is determined by its input/output characteristic, itsinterconnection to other units, and eventually its interconnection toexternal inputs. The network usually develops an overall functionalitythrough one or more forms of training. Supervised learning neuralnetwork has been used for developing the data mining process. Insupervised learning a set of typical input/output mappings forms adatabase-denoted training set, which provides significant information onhow to associate input data with outputs. The training set is referredto as segments.

The knowledge required to map input pattern into an appropriate outputpattern is embedded in the connection weights inside the neural network.Initially the weights are unknown. Until a set of applicable weights isfound the network has no ability to deal with the problem to be solved.A dynamic data point (DDP) for referring to data from each sensormaintains database connection information for any given sensor, i.e.data base server name, table name and column name. A dynamic data pointis a relation between a physical sensor and a location of data within adatabase. Therefore DDPs are preferably created for all sensors. Sensorswith physical or statistical relations are selected and grouped; theoutcome of this selection is referred to as sensor segmentation. Ofcourse, for large sensor systems, hierarchical segmentation is possibleand often preferred. Grouping of sensors is achieved by selecting asensor segment with one or more sensors from this sensor segment, forthe purpose of validation.

The resulting application provides assistance for performing andachieving data quality assurance, i.e. data/sensor validation.Optionally, sensors are strategically installed over an area undersurveillance, and the sensors wirelessly communicate with a satellitethe sensed data to be transmitted at intervals through a neural network.Using a Geographic Information Systems (GIS) or a GPS interface allowsfor tracking of sensors, which improves the identification of redundantdata sensors. For example, sensors sensing similar locations are morelikely to sense redundant information. As such, grouping of sensorsbased on geography is a powerful tool. Using a GIS interface, allows fororganization of sensors into groups relating to their proximity andsimilarity. The groups are then organized into larger groups and soforth. Thus, the system provides a powerful tool to facilitate trainingof the correlation processor. Also, using a GIS interface improves thecorrelation processor's ability to adapt and improve over time inresponse to environmental changes and constraints.

Referring to FIGS. 4a and 4 b, a method of training a neural network isshown. For each group of sensors as defined in FIG. 3, data are read.For each sensor records appropriate time steps are used to fill inmissing data with the values −1, which is the number used to representmissing values. Depending upon the type of data, as for example anunderflow or an overflow, with corresponding values of missing databased on historical data. When a prediction model is used during thetraining process, for each sensor a separate prediction model isprepared using historical data.

The following table, table 1, is an example of the preparation of inputand output patterns from one sensor. Segments corresponding to set ofcontinuous data that do not contain any missing values are created. Asize of each segment is preferably greater than a pre-determined numberof data sensed within a predetermined period of time, which represents adelay, plus one data. A moving average over all individual segments isperformed provided the segment size is greater than twice thepre-determined of data sensed within a predetermined period of time. Aninput and output for each segment is prepared provided the segment sizeis greater than twice the pre-determined number of data sensed within apredetermined period of time. An input and output pattern is preparedfrom each segment and merged together to form what is referred to as thetraining input and output pattern for the prediction model.

In the example illustrated in table 1, the delay is 4. Segment 2 hasmore than 4 values (delay) plus one value; therefore segment 2 wascreated. However, segment 2 is ignored in preparing the input and outputpattern because its size is less than two delays.

TABLE 1

Of course, each input-output pair is preferentially from the samesegment.

The prediction model is trained, allowing for useful connection weightsto be obtained through iterative learning. Typically, the connectionweights are stored and represent a prediction model. The iteration stopseither when acceptable minimum mean square error is achieved betweenpredicted output values and known output values or when a predeterminedmaximum number of iterations of the training process are reached.

The prediction model is used to fill in missing data in each sensorprior to using them to prepare the regression model. Only input data arepre-processed and the prediction model provides with the output, whichreplaces the missing value. Referring back to the Table, for example, inorder to fill in the first missing value (delay) on the ninth raw,values just preceding the missing values are used as input data andsubmitted to the prediction model, the prediction model then provides anoutput that replaces the value −1.

In the process of replacing missing values, some criteria exist, and thepercentage of appearance of a missing data, in one of the sensor data,is a useful parameter. For example, if it is less than 30%, theprediction model restores the data without warning and the consequentprocess continues. If it is comprised of between 30% and 60%, theprediction model fills the missing data and the process continues, theoperator is nonetheless warned regarding this high percentage of missingdata. However, if it is greater than 60% nothing is done and theoperator is notified.

All sensors measurements are scanned and n delay continuous measures arefound. During pre-processing, data are then smoothed using movingaverage techniques. The training input data and desired output data aregenerated for the regression model. Input/output patterns are preparedfrom each group of sensors. The input pattern contains all the sensorreadings in the group and the output pattern contains the sensor to bevalidated.

Generating a regression model involves the step of preparing from eachsensor, input and output patterns to create segments, i.e. sets ofcontinuous sets of data that do not contain any missing values. A sizeof each segment is preferably greater than a pre-determined number ofdata sensed within a predetermined period of time, which represents adelay, plus one data. A moving average over all segments is individuallycalculated, provided the segment size is greater than twice the timerequired to predict a missing value. The input pattern and outputpattern are prepared for each segment, provided the segment size isgreater than twice the time required to predict a missing value. Then,from each segment the input pattern and output pattern are prepared andmerged together to form the training input and output pattern for theregression model. Each input-output pair is preferably from the samesegment.

Alternatively, when data each sensor records individually does notprovide more information to the neural network learning process then itis often efficient to work on a sum of similar type sensor records. Thussimilar sensor data of a corresponding time step are added together andthe neural network is trained based on the cumulative data. Summingsensor data is preferably only performed for the regression model.

The regression model is trained—useful connection weights are obtainedthrough an iterative training process. Typically, the connection weightsare stored and they, as a group, represent the regression model. Theiteration of the training process stops either when acceptable minimummean square error is achieved between known output data and predicteddata or when a predetermined number of iterations have been performed.

A neural network using cross validation is created when available, andthe neural network prediction is trained for each sensor and the weightsfor each sensor are saved. Furthermore, the neural network regressionfor the segment of sensors is also trained and the weights for thesegment of sensors are saved.

The last step of the training process consists in recreating theprediction models based on the restored data; a restored data representsensor data whose missing values are filled by the prediction model.This is achieved in order to get a fine-tuned prediction model usableduring the testing process.

Referring to FIG. 5, a flow chart diagram of a testing process accordingto the present invention is shown. Using gathered knowledge from thestep of training, the neural network is executed for validating receivedsensor data. The sensor data used for testing are different from thesensor data that were used in the training process. Usually the sensordata to be validated are data that were acquired from known operationalsystem components immediately after the training data in terms of timehistory. A test data for each sensor is taken to calculate a number ofrecords in a specified time interval. Appropriate time steps are used tofill in missing time steps with values of missing data—in the presentimplementation, a value of −1 is used to represent missing values.

The prediction weight files, which are representative of the predictionmodel stored upon completion of the training process, are loaded. Theprediction model is used to predict missing data for each sensor priorto using the sensors in the validation process by the regression model.The weights for each sensor, and the weight for each segment of sensorsare loaded for use in executing the neural network prediction model toestimate missing data.

The weights from the regression model are loaded, then test data areprovided to the network in order to execute the network regression modelto get the outputs values.

Alternatively, if a sum sensor was checked during the training process,then the sensor data are summed as previously described and thevalidation is performed according to the summed data.

Using conventional statistical analysis methodology, calculation ofresidue, mean and standard deviation is performed for the data. Athreshold is set using the statistical data and potentially erroneousdata are detected or excluded by calculating a confidence factor. Iferroneous data are detected, flags are set for the operator to indicatesuch.

Selection of sensors having statistical correlation is typicallysomewhat straightforward though the system has an ability to use lessthan perfectly grouped data. Alternatively, an automated system forselecting statistically correlated sensors is used such as a geneticalgorithm or an expert system.

Because the neural network is capable of determining correlationsbetween sensors through a process of training, the individualcorrelations or redundancies need not be analyzed and predicted by theoperator. As such, a method and system according to the presentinvention is easier to use and to segment than known prior art methods.

Advantageously, the data mining process according to the inventionprovides a system that is executable as soon as it is installed andwhich does not necessitate a long training period for providing a mostappropriate reaction to an event.

The operation of the neural network is based on the prediction and theregression models that are both trained. Therefore, data sent to theneural network have already been pre-processed.

Advantageously, using such a system provides enough confidence in thepre-processing that it allows for suggesting replacement data with ahigh degree of confidence. Based on the prediction and the regressionmodels, when sensed data of some sensors are off a predictable modelaccording to a regression obtained based on historical data, the modelsuggests for replacing the “off-line” value with a most probable value.An off value does not mean that the sensor has failed and has to bereplaced.

Of course, if this is not an isolated occurrence, appropriate actionsare taken.

Numerous other embodiments may be envisaged without departing from thespirit and scope of the invention.

What is claimed is:
 1. A method of data sensor validation comprising thesteps of: pre-processing data sensor from each sensor from a pluralityof sensors for at least segmenting the data sensors into a plurality ofgroups, each group for grouping sensors for sensing highly relevant dataone to another; providing the pre-processed data sensor to a correlationprocessor, the correlation processor for determining from pre-processeddata sensor, pre-processed data that is other than correlated, thedetermination made in dependence upon redundant pre-processed data otherthan pre-processed data from two sensors for sensing an identicalparameter; and, when pre-processed data that is other than correlated isdetected, providing an indication to an operator that the sensor data isother than correlated.
 2. A method according to claim 1, wherein thestep of pre-processing data sensor comprises the steps of: generating aprediction model for each sensor from the plurality of sensors, theprediction model for forming input patterns and output patterns basedupon sets of continuous sensed data; iteratively training the predictionmodel, the iteration performed when a pre-determined amount of data aresensed, the iteration performed until a pre-determined level is reached;generating a regression model for each sensor from the plurality ofsensors, the regression model for forming input patterns and outputpatterns based upon sets of continuous sensed data; iteratively trainingthe regression model, the iteration performed when a pre-determinedamount of data are sensed, the iteration performed until apre-determined level is reached; and, providing pre-processed data independence upon at least the regression model to the correlationprocessor.
 3. A method according to claim 2 wherein the step ofiteratively training the prediction model comprises the steps of:creating connection weights; and storing the connection weights suchthat they are indicative of the prediction model.
 4. A method accordingto claim 2, wherein the step of iteratively training the regressionmodel comprises the steps of: creating connection weights; and storingthe connection weights such that they are indicative of the regressionmodel.
 5. A method according to claim 2, wherein the pre-determinedlevel is when an acceptable minimum mean square error is achieved.
 6. Amethod according to claim 2, wherein the pre-determined level is when amaximum pre-specified iteration is reached.
 7. A method according toclaim 1, wherein the step of segmenting the data sensors into aplurality of groups comprises the step of determining one of physicaland statistical relationship between data sensors in dependence upon adynamic data point.
 8. A method of data sensor validation according toclaim 7, wherein the step of segmenting the data sensors comprising thesteps of: sizing each group from the plurality of groups such that asize corresponds to a pre-determined number of sensor data within apredetermined period of time; and, performing a moving average of eachgroup from the plurality of groups provided a group size is greater thantwice the pre-determined number of sensor data within a predeterminedperiod of time.
 9. A method of data sensor validation according to claim7, wherein the step of segmenting the data sensors into a plurality ofgroups comprises the step of generating an input pattern and an outputpattern for each group of the plurality of groups, each of the inputpattern and of the output pattern comprising continuous sets of sensordata.
 10. A method of data sensor validation according to claim 9,wherein the step of generating an input pattern and an output patterncomprises the step of merging the input pattern and the output patternfrom each group for forming a training input and output pattern for usewith a prediction model.
 11. A method of data sensor validationaccording to claim 7, wherein the correlation processor is coupled forreceiving the pre-processed data and for processing the pre-processeddata to determine a correlation between pre-processed data from eachsensor within a same group; and, wherein the step of determining one ofphysical and statistical relationship comprises the step of performingtraining of the correlation processor based on a plurality of differentsegmentations of the plurality of data sensors to determine asignificant grouping.
 12. A method of data sensor validation accordingto claim 2, wherein the step of generating a prediction model comprisesthe step of filling in a missing value.
 13. A method of data sensorvalidation according to claim 11, wherein the correlation processor isfor processing correlations between different groups as well.
 14. Amethod of data sensor validation according to claim 13, wherein thecorrelation processor is for processing correlations data immediatelyafter the step of providing data transformed in dependence upon at leastthe regression model to the correlation processor.
 15. A methodaccording to claim 13, wherein the correlation processor is a neuralnetwork.
 16. A method of data sensor validation according to claim 1,wherein the correlation processor is a neural network.
 17. A method ofdata sensor validation according to claim 11, wherein the pre-processeddata that is other than correlated is pre-processed data that representsa physical parameter that is inconsistent with other sensor data, theother sensor data received from data sensors segmented within a samegroup of data sensors.
 18. A method of data sensor validation accordingto claim 17, wherein the pre-processed data that is other thancorrelated is pre-processed data that represents a physical parameterthat is inconsistent with other pre-processed data, the otherpre-processed data determined from data received from data sensorssegmented within a different group of data sensors.
 19. A method of datasensor validation according to claim 1, wherein the step ofpre-processing data sensor from each sensor from a plurality of sensorscomprising the step of suggesting a most probable data for use when datasensor are off a predictable range of data sensor according to datasensor from other sensors from the plurality of sensors.
 20. A method ofdata sensor validation according to claim 17, wherein sensors areenvironmental sensors.
 21. A method according to claim 20, wherein theenvironmental sensors include a hydrosensor for sensing information andproviding data relating to at least one of waterflow and waterlevels.22. A method according to claim 21, wherein the data is correlated forsensor validation in a water level control system including a pluralityof dams and interconnected waterways.
 23. A sensor for use ingeographically remote sensor applications comprising: a sensingcircuitry for sensing data; a transmitter for transmitting sensed datato a correlation processor, the correlation processor for determiningfrom pre-processed sensed data, pre-processed data that is other thancorrelated, the determination made in dependence upon redundantpre-processed data other than pre-processed data from two sensors forsensing an identical parameter at an approximately same geographiclocation; and a wireless transceiver circuit for wirelessly determininga geographic location of the sensor, for transmitting the determinedgeographic location of the sensor to the correlation processor, and fortransmitting the sensed data to the correlation processor for allowingthe correlation processor to associate the received sensed data with thedetermined geographic location.
 24. A sensor for use in geographicallyremote sensor applications according to claim 23, wherein thecorrelation processor is a neural network.
 25. A sensor for use ingeographically remote sensor applications according to claim 23 whereindata sensed by the sensor is environmental data.
 26. A sensor for use ingeographically remote sensor applications according to claim 25, whereinthe environmental data sensed by a sensor is determined upon thegeographical location of the sensor.
 27. A sensor according to claim 23,wherein the environmental sensor is a hydrosensor for sensinginformation and providing data relating to at least one of waterflow andwaterlevels.
 28. A sensor according to claim 27, wherein the correlationprocessor is for sensor validation in a water level control systemincluding a plurality of dams and interconnected waterways.
 29. A sensorfor use in geographically remote sensor applications according to claim23, wherein the wireless transceiver circuit comprises a globalpositioning system for determining geographic location of the sensoraccording to coordinates receive from satellites.
 30. A method of datasensor validation comprising the steps of: pre-processing data sensorfrom each sensor from a plurality of sensors; providing thepre-processed data sensor to a correlation processor, the correlationprocessor for determining from pre-processed data sensor, pre-processeddata that is other than correlated, the determination made in dependenceupon redundant pre-processed data other than pre-processed data from twosensors for sensing an identical parameter; and, when pre-processed datathat is other than correlated is detected, providing an indication to anoperator that the sensor data is other than correlated.